A new two-phase approach to the portfolio optimization problem based on the prediction of stock price trends

Advances in Mathematical Finance and Applications

مقاله 2، دوره 7، شماره 4، دی 2022، صفحه 849-865   

نوع مقاله: Research Paper

شناسه دیجیتال (DOI): 10.22034/amfa.2021.1937022.1622

نویسندگان

Hamid Reza Yousefzade^{* 1}؛ Amin Karrabi²؛ Aghileh Heydari³

¹Department of Mathematics, Payame Noor University (PNU), Tehran, Iran

²Department of Mathematics, Payam noor University, Mashhad, Iran

³Department of Mathematics, Payame Noor University (PNU), P.O. BOX 19395-4697, Tehran, Iran.

چکیده

Forming a portfolio of different stocks instead of buying a particular type of stock can reduce the potential loss of investing in the stock market. Although forming a portfolio based solely on past data is the main theme of various researches in this field, considering a portfolio of different stocks regardless of their future return can reduce the profits of investment. The aim of this paper is to introduce a new two-phase approach to forming an optimal portfolio using the predicted stock trend pat-tern. In the first phase, we use the Hurst exponent as a filter to identify stable stocks and then, we use a meta-heuristic algorithm such as the support vector regression algorithm to predict stable stock price trends. In the next phase, according to the predicted price trend of each stock having a positive return, we start arranging the portfolio based on the type of stock and the percentage of allocated capacity of the total portfolio to that stock. To this end, we use the multi-objective particle swarm optimization algorithm to determine the optimal portfolios as well as the optimal weights corresponding to each stock. The sample, which was selected using the systematic removal method, consists of active firms listed on the Tehran Stock Ex-change from 2018 to 2020. Experimental results, obtained from a portfolio based on the prediction of stock price trends, indicate that our suggested approach outperforms the retrospective approaches in approximating the actual efficient frontier of the problem, in terms of both diversity and convergence.

کلیدواژه‌ها

Multi-objective optimziation؛ Support vector regression (SVR)؛ ‎Multi-objective particle swarm optimization (MOPSO)؛ Efficient Frontier

مراجع

[1] Alahmari, S., Predicting the Price of Cryptocurrency using Support Vector Regression Methods, Journal of Mechanics of Continua and Mathematical Sciences, 2020, 15(4), P. 313-322.

[2] Asgharpur, H., Rezazadeh, A., Determining the Stock Optimal Portfolio using Value at Risk, Journal of Applied Theories of Economics, 2016, 2(4), P.93-118, (in Persian).

[3] Ballestero, E., Mean-Semi-Variance Efficient Frontier: A Downside Risk Model for Portfolio Selection, Applied Mathematical Finance, 2005, 12(1), P. 1-15.

[4] Bernardo, J. A., Rui Ferreira, N., Nuno, H., Combining Support Vector Machine with Genetic Algorithms to optimize investments in Forex markets with high leverage, Applied Soft Computing, 2018, 64(2), P. 596–613.

[5] Coello, A. C., Pulido, G. T., Lechuga, M. S., Handling multiple objectives with particle swarm optimization, In IEEE transactions on evolutionary computation, 2004, 8(3), P. 256-279.

[6] Das, S., Arman, M. S., Hossain, S.S., Islam, S., Bangladeshi Stock Price Prediction and Analysis with Potent Machine Learning Approaches, Cyber Security and Computer Science, 2020, 325(3), P. 230-240.

[7] Fernandez, A., Gomez, S., Portfolio selection using neural networks, Computers operation Research, 2007, 34(4), P. 1177-1191. 

[8] Ghasemi, H. R., Najafi, A. A., Portfolio Optimization in terms of Justifiability Short Selling and Some Market Practical Constraints, Financial Research Journal, 2014, 14(2), P. 117-132, (in Persian).

[9] Gilli, M., Kellezi, E., The Threshold Accepting Heuristic for Index Tracking, Financial Engineering, E-commerce and Supply Chain, 2001, 70(2), P. 1-18.

[10] Guang-Feng, D., Woo-Tsong, L., Ant Colony Optimization for Markowitz Mean-Variance Portfolio Mode, Swarm, Evolutionary and Memetic Computing Lecture Notes in Computer Science, 2010, 6466(2), P. 238-245.

[11] Kara, Y., Boyacioglu, M. A., Baykan, O. K., Predicting direction of stock price index movement using artificial neural networks and support vector machines: the sample of the Istanbul Stock Exchange, Expert Systems with Applications, 2011, 38(5), P. 5311-5319.

[12] Kennedy, J., Eberhart, R., A New Optimizer Using Particle Swarm Theory, In Sixth international symposium on micro machine and human scienc, 1995, P. 39-43.

 [13] Kim, K., Financial time series forecasting using support vector machines, Neuro-computing, 2003, 55(1), P. 307-319.

[14] Li G.Z., Huang J.B. & Huang H.Y. Calculating method of contraction operators in fractal interpolation based on the B-spline, Journal of Ordnance Engineering College, 2006, 18(2), P.76-78.

 [15] Mansini, R., Speranza, M. G., Heuristic Algorithms for the Portfolio Selection Problem with Minimum Transaction Lots, European Journal of Operational Research, 1999, 114(2), P. 219–233.

 [16] Maringer, D.‎,‎ Portfolio Management with Heuristic Optimization, Advances in Computational Management Science, Published by Springer-Verlag, Berlin, Heidelberg, 2006, P. 1–237.

[17] Markowitz, H. M., Portfolio Selection, The Journal of Finance, 1952, 7(1), P. 77-91.

[18] Reyes Sierra M., Coello, C., Multi Objective Particle Swarm Optimizers: A Survey of the State of the Art, International Journal of Computational Intelligence Research, 2006, 2(3), P.287-308.

[19] Sahala, A. P., Hertono, G. F., Handari, B. D., Implementation of improved quick artificial Bee Colony Algorithm on portfolio optimization problems with constraints, Proceedings of the 5th International Symposium on Current Progress in Mathematics and Sciences, 2020, 2242(1), P.1–8.

[20] Ünal, A., Kayakutlu, G., Multi-objective particle swarm optimization with random immigrants, Complex & Intelligent Systems, 2020, 6(2), P. 635–650.

[21] Vasiani, V., Handari, B.D., Hertono, G.F., Stock portfolio optimization using priority index and genetic algorithm, Journal of Physics: Conference Series, 2020, 1442, P. 1-5.

[22] Xu Jun., Effectiveness of the Securities Market Analysis Efficient Market Hypothesis Testing of Shanghai Stock Exchange by the Method of Rescale Range. Wuhan University, 2004.

[23] Yoosefzade, H.R., Karrabi, A., Heydari, A., Fracsion: New Hybrid Algorithm Predicting the Trend of Tehran Stock Exchange Industries Index, Journal of Mathematical Researches, 2021, Accepted paper (in Persian).